Mid latitude ionospheric TEC modeling and the IRI model validation during the recent high solar activity (2013–2015)

Available online at www.sciencedirect.com

ScienceDirect Advances in Space Research 63 (2019) 4025–4038 www.elsevier.com/locate/asr

Mid latitude ionospheric TEC modeling and the IRI model validation during the recent high solar activity (2013–2015) Yekoye Asmare Tariku Space Science and Research Applications Development Department, Ethiopian Space Science and Technology Institute, Addis Ababa, Ethiopia Received 6 December 2018; received in revised form 8 March 2019; accepted 9 March 2019 Available online 19 March 2019

Abstract This paper discusses the variability of the modelled Vertical Total Electron Content (VTEC) and performance of the latest versions of the International Reference Ionosphere (IRI) model in the estimation of TEC over the mid-latitude regions in the recent high solar activity (2013–2015) years. This is conducted by comparing the pattern of the variations of the VTEC obtained from five ground based Global Positioning System (GPS) receivers installed at different mid-latitude regions and the latest versions of the IRI model (IRI 2007, IRI 2012 and IRI 2016). It has been observed that the measured (GPS-derived) and modelled (IRI 2007, IRI 2012 and IRI 2016) monthly and seasonal diurnal variability of VTEC show the lowest values at around 10:00 UT (04:00 LT) and the highest values at around 20:00 UT (14:00 LT). Moreover, both the measured and modelled VTEC variations generally follow the pattern of the variation of the solar activity, showing enhancement in shifting from 2013 to 2014, and drop again in 2015, with some exceptional months. In the years 2013– 2015, the highest measured and modelled seasonal arithmetic mean VTEC values are observed in the March equinox in 2014; while, the lowest measured and modelled VTEC values are observed in the December solstice and June solstice, respectively in 2015. It has also been shown that the IRI 2016 VTEC values are generally larger than those of the IRI 2007 and IRI 2012 VTEC values and tend to respond to variation of the GPS VTEC values better than others, especially in the equinoctial and June solstice months. Moreover, when compared to the IRI 2007 and IRI 2012 versions, the smallest root-mean-square deviations are observed in using the IRI 2016 version, showing that the IRI 2016 version is generally better in capturing the VTEC values with some exceptional months (especially in the December solstice months). Hence, in the December solstice months, the IRI 2007 and IRI 2012 versions are generally better in estimating the VTEC variations as compared to the IRI 2016 version. Moreover, the root-mean-square deviations obtained (either due to overestimations or underestimations) in the IRI 2007 and IRI 2012 versions are very close to each other, proving that the two versions show similar performance in TEC estimation. Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: GPS VTEC; IRI 2007 VTEC; IRI 2012 VTEC; IRI 2016 VTEC; High solar activity; Mid latitude ionosphere

1. Introduction The upper atmosphere of the Earth is characterized by large population of ions and electrons which can largely influence radio wave propagation (Matsushita and Wallace, 1967). As a result, the ionosphere is a very dynamic region which shows diurnal, monthly and sea-

E-mail address: [email protected] https://doi.org/10.1016/j.asr.2019.03.010 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.

sonal variability. Solar and geomagnetic activity variations are also common phenomena in the region. According to Ezquer et al. (1994, 1997), the radio signals are affected by the free electrons existing from the receiver on the ground to the orbiting satellites (usually called the Total Electron Content, TEC) while traversing the ionosphere. Propagation delay and refraction of the trans-ionospheric wave are some of the effects resulting from the TEC (Kailang and Jianming, 1994). Thus, in order to accurately determine the satellite positions, ionospheric corrections

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requiring TEC measurements have to be implemented along the ground-satellite path (Hartman and Leitinger, 1984). When signals are transmitted from the GPS satellite to receiver on the ground through the ionosphere, their speed and direction of propagation are strongly affected by the TEC. Consequently, the signals cannot arrive at the receivers on the ground in the scheduled time (Ioannides and Strangeways, 2000). This may result in range-rate errors for the GPS satellites users that need to have high accuracy measurements (Bradford and Spilker, 1996). To better understand the ionosphere, ground or space based instruments (such GPS) have been so far extensively used. Since the GPS receiver measurements are important in the estimation of the TEC, the GPS has been employed globally to study the variation of TEC in the ionosphere (Mannucci et al., 1998). In addition, in a region where measured values are not available, ionospheric empirical models (such as the IRI model) also play a major role in the estimation of ionospheric TEC. To evaluate the model’s performance in the estimation of the ionospheric TEC, many comparative studies have been conducted between the GPS TEC and IRI TEC over the mid latitude regions employing different versions of the model. For instance, McNamara (1985), Bilitza et al. (1999), Chakraborty et al. (2014), Kumar et al. (2015), Tariku (2016) and Gordiyenko and Yakovets (2017) noted that the estimation of the VTEC values by the IRI model is good in the mid latitude regions, mainly because the input data used by the model are obtained mostly from the midlatitude regions. For instance, McNamara (1985) reported that the IRI model fails to reproduce the observed values of the TEC at low latitude regions, but show good performance at the mid-latitude ionosphere. Similarly, Gordiyenko and Yakovets (2017) show that the IRI 2012 model describes well the morphology of seasonal and diurnal variations of the ionospheric critical frequency (foF2) and peak density height (hmF2) monthly medians over the mid-latitude in the low solar activity period. This shows that the model performance in the estimation of TEC is good in the mid-latitude regions. Moreover, Kumar et al. (2015) observed that the discrepancies between the IRI 2012 VTEC and GPS VTEC values are small, showing that the model performance in estimating the TEC is good in the mid latitude regions. However, nobody has so far adequately studied and compared the pattern of the variability of both the measured (GPS TEC) and the modelled (IRI TEC) over the mid latitude regions during the solar maximum phase using the recently released model versions (IRI 2007, IRI 2012 and IRI 2016). Because most of the past studies have been reported typically with the old versions of the model, the performance of the recent versions of the IRI model (especially IRI 2016) has not been adequately tested over the mid latitude regions during different solar activity periods. Only few researches were conducted over the region to test the performance of the model using the most recent version, IRI 2016. Thus, the study is vital to observe the improvement of performance of the model

in order to use it in some occasions when measured values are scarcely available in the region. It is also supposed to be beneficial to the IRI developers and other empirical modelers. Consequently, this is perhaps the first work to extensively assess the monthly and seasonal VTEC variations and compare the performance of the IRI model in estimating the VTEC over the middle latitude regions during the recent high solar activity years (2013–2015). 2. Data description and analysis method 2.1. TEC from dual frequency GPS receivers To enhance the precision of TEC measurements employing GPS, it is vital to estimate the satellite and receiver differential biases that can corrupt the TEC measurements (Mannucci et al., 1998; Ciraolo et al., 2007). In the estimation of TEC, dual frequency receivers are used to better eliminate ionospheric errors (Klobuchar, 1996). In addition, as shown in Ciraolo et al. (2007) and Nahavandchi and Soltanpour (2008), such receivers are able to give better information about the ionosphere and plasmasphere by calculating the differential of the code and carrier phase measurements. Thus, dual frequency receivers have been used to get the required GPS-TEC data employing pseudo-range and carrier phase measurements for this study. The TEC inferred from the pseudo-range (P) measurement is given by: " # 1 f 21 f 22 STEC P ¼ ðP 2  P 1 Þ: ð1Þ 40:3 f 21  f 22 Using similar approach, the TEC from carrier phase measurement (U) is given as " # 1 f 21 f 22 ðU1  U2 Þ; STEC U ¼ ð2Þ 40:3 f 21  f 22 where f 1 and f 2 can be expressed in terms of the fundamental frequency, f o ¼ 10:23MHz f 1 ¼ 154f o ¼ 1575:42 MHz; f 2 ¼ 120f o ¼ 1227:60 MHz:

ð3Þ

As shown above, the GPS receivers in the global network generate two observables for each satellite being tracked: pseudo range delay (P) and carrier phase advance (U). The frequency-differenced phase delays provide very precise measurements of STEC changes, but contain an overall bias associated with integer cycle ambiguities. On the other hand, the frequency-differenced pseudo-range data provide an absolute measure of the total ionospheric delay between satellite and receiver, but have significantly more multipath and system noise than the phase-based data (Gao and Liu, 2002). Thus, linearly combining both code pseudo range and carrier phase measurements is

Y.A. Tariku / Advances in Space Research 63 (2019) 4025–4038

important to enhance the degree of accuracy of TEC. This resultant absolute TEC along the path from the satellite to the receiver on the ground is the slant TEC (GPS STEC). As the determined STEC in this way relies on the ray path geometry from the satellite to the receiver through the ionosphere, it is vital to convert the STEC to a ray path independent TEC (VTEC) employing a mapping function. In using a mapping function it is assumed that the majority of electron density in the mid-latitude regions is concentrated in a thin layer with height interval of 250–350 km above the surface of the Earth in the two dimensional spherical shell model, as shown in the figure (Mannucci et al., 1998).

Thus, using a simple mapping function which is associated to an ionospheric pierce point (IPP) latitude and longitude, where the ionosphere is assumed to be compressed into a thin shell at the mean ionospheric height of 300 km, the line-of-sight of STEC values are converted to VTEC values as described in Norsuzila et al. (2008, 2009), Hence, using the above figure, the relationship between STEC and VTEC can be given by: VTEC ¼ STEC ðcosv0 Þ

ð4Þ

Using the sine law, we have. v0 ¼ arcsin½

Re sinv: Re þ h m

ð5Þ

Plugging Eq. (5) into Eq. (4) and making rearrangement, we obtain      Re VTEC ¼ STEC cos arcsin sinv : ð6Þ Re þ hm Here, Re represents Earth’s radius in kilometers. Moreover, v0 and v are zenith angles at the ionospheric piercing point (IPP) and the receiver position, respectively. Since it is more compacted than the STEC, the VTEC is used as a good indicator for the overall ionization of Earth’s ionosphere (Komjathy and Langley, 1996).

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2.2. TEC from the international reference ionosphere (IRI model) As the experimental TEC data (such as GPS-derived TEC data) cannot be always available from all regions on the Earth, some modelled data are vital to understand the global variability of TEC. Hence, for such occasions, different empirical models (such as IRI) are commonly used for the estimation of TEC (Bilitza, 2001, Bilitza and Reinisch 2008a). The IRI model, like other empirical models, is useful for the purpose of prediction of different parameters (such as TEC) in the ionosphere. It is important in providing the TEC and average values of electron density for magnetically quiet conditions. Because of this improvements have been steadily made to enhance the model’s performance employing different data available in the globe (Bilitza, 2003). As described by Aggarwal (2011), incoherent scatter radars, the worldwide network of ionosondes, the ISIS and Alouette topside sounders and rockets are the major sources of data for the model. As a result, the relatively new versions of the model (IRI 2007, IRI 2012 and IRI 2016) have been released in 2008, 2014 and 2017, respectively by incorporating some new amendments that can enhance its performance in the estimation of ionospheric TEC (Bilitza and Reinisch 2008b; Bilitza et al., 2014, 2017). For instance, starting from version 2007, the topside options for the electron density (NeQuick, IRI01-corr and IRI2001) that are supposed to improve the prediction of the electron density (or TEC) for the topside ionosphere have been incorporated. In addition, the IRI 2007, IRI 2012 and IRI 2016 versions have Gulyaeva (Gul-1987) option in common for the bottomside thickness that was not included in the previous versions of the IRI model. Bil-2000 and ABT-2009 options for the bottomside thickness option have also been included in IRI 2012 and IRI 2016 which did not exist in the previous versions, including the IRI 2007 version. Moreover, year, month, time, location are basic input parameters that were also used in the previous versions of the model. Sunspot number (R12), F10.7 radio flux, Ne F-peak options (CCIR for the content and URSI for ocean areas) are also optional input parameters embedded in different version of the IRI model (including IRI 2007, IRI 2012 and IRI 2016). The IRI 2016 version, in particular, includes two new model options for the F2-peak height hmF2 and a better representation of topside ion densities at very low and high solar activities. The two options enable the IRI 2016 model in estimating hmf2 directly and no longer through its relationship to the propagation factor M(3000)F2. As a result, the IRI 2016 model can estimate evening peaks that was not possible in the old versions, including the IRI 2007 and IRI 2012. In addition, the IRI 2007, IRI 2012 and IRI 2016 models (similar to the previous versions) provide the monthly average TEC in the altitude reaching about 50 to 2000 km (Bilitza et al., 2007, 2014, 2017; http:// IRImodel.org.).

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2.3. Data and analysis method To get the required data, the recent solar maximum (2013–2015) years were considered for both GPS and the model. The GPS TEC data were derived from the ground based dual frequency GPS receiver located at the mid latitude regions shown in Table 1. The stations are selected based on the availability of the required GPS TEC data in the described years. Moreover, the main objective of the study is to assess the time variation of the modelled TEC and performance of the three versions (IRI 2007, IRI 2012 and IRI 2016) of the IRI model without considering the spatial aspects. The GPS data from the station have been downloaded from the UNAVCO web site (http://www.unavco.org/). Since the data are stored in the RINEX format, the RINEX GPS-TEC program version 2.9.5 developed by Gopi Seemala (Seemala, 2017) has been used to derive the required GPS TEC data from the RINEX files. Using NeQuick option for the topside electron density, the IRI VTEC data were also obtained from the latest versions of the online IRI model (IRI 2007, IRI 2012 and IRI 2016) available at https:// ccmc.gsfc.nasa.gov/modelweb/models/iri2007_vitmo.php, https://ccmc.gsfc.nasa.gov/modelweb/models/iri2012_vitmo. php, and https://ccmc.gsfc.nasa.gov/modelweb/models/ iri2016_vitmo.php, respectively. Moreover, the CCIR option has been used for the F2 peak density. In general, for all versions of the model, common input and output parameters that have impact on the variation of TEC were used. In addition, to see the monthly and seasonal diurnal variations, the hour-to-hour values of the VTEC have been added and averaged for each month and season. Similarly, the monthly hour-to-hour measured and modelled VTEC values have been correspondingly added and averaged for the whole magnetically quiet days in each month and season to observe the monthly and seasonal arithmetic mean VTEC variations. Here, the magnetically quiet days are those with Dst index > -30 nT based on the reference: Dst < -100 nT-intense storm, 50 nT < Dst < -100 nTmoderate storm and 30 nT < Dst < -50 nT-weak storm (see Gonzalez et al., 1994). The geomagnetic data have been downloaded from http://facility.unavco.org/data/ dai2/app/dai2.,http://wdc.kugi.kyoto. The seasons can be categorized as June solstice (May, June and July), September equinox (August, September and October), December solstice (November, December and January) and March

equinox (February, March and April). The root-meansquare deviations between GPS VTEC and IRI VTEC: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 measured Þ D ¼ ðVTEC model VTEC , with N refers the number of data N points have been determined for the seasonal diurnal variations. Moreover, the percentage differences

jIRI VTECGPS VTEC jÞ IRI VTEC

 100 for both the monthly and sea-

sonal arithmetic mean VTEC variations have also been calculated. The GPS STEC calibration process was performed in five minutes interval and an elevation cut-off 20° was used to reduce the error emanating from multipath effects. On the other hand, the IRI VTEC values are directly extracted from the IRI online model after the required input parameters are filled. 3. Results and discussion 3.1. Diurnal monthly and seasonal variations of VTEC and performance of the IRI model The results of the monthly and seasonal diurnal variations are shown in Figs. 1–10. As shown in the figures, the diurnal VTEC values start decreasing after 00:00 UT (18:00 LT) and become minimum at around 10:00 UT (04:00 LT). However, after 10:00 UT (04:00 LT) their values start increasing and attain the corresponding peak values in the daytime hours (nearly at 20:00 UT, 14:00 LT). After 20:00 UT (14:00 LT), the TEC values again begin to decrease. This gradual increment of the VTEC in the daytime hours to reach the peak value at the noontime hours may be due to the variation of solar EUV (Ezquer et al., 2008, 2014). Since the solar EUV radiation is synchronized with the TEC, the peak VTEC values increase linearly with the EUV peaks at noon time hours (Balan et al., 1994). Moreover, as shown in the Figures (see Figs. 1–10), in the years 2013–2015 both the measured and modelled VTEC values generally follow the trend of the variation of the solar activity, showing enhancement in shifting from 2013 to 2014 and start to decrease in 2015 again, with some exceptional months. For instance, the maximum peak hour-to-hour monthly measured value of about 32 TECU being observed in March in 2013 increases to 58 TECU in 2014 and reduces to about 45 TECU in the same month in 2015. Similarly the highest peak hour-to-hour IRI 2016 VTEC values of about 30 TECU being observed in February during 2013 rises to

Table 1 Coordinates of GPS receivers used for the study. Code

Station Name

Geographic coordinates Lat. (N) °, Long. (W)°

Geomagnetic coordinates Lat. (N), Long. (W)

CJTR CVMS STLE PTGV MACC

Camp JT Rabinson Army Nat. Guard Crestview Middle School US Supply Handling Equipment Delta Research Center Mineral Air community college

(34.82,267.73) (35.54,270.36) (36.09,270.14) (36.41,270.3) (37.85,269.52)

(44.53, (46.29, (46.82, (47.15, (48.52,

22.60) 19.06) 19.36 19.14) 20.22)

Y.A. Tariku / Advances in Space Research 63 (2019) 4025–4038

20

5

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Fig. 1. Monthly diurnal VTEC variation and validation of the IRI model over Mineral Area community College (MACC) in 2013.

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Fig. 2. Root-mean-square deviations between the monthly GPS and IRI VTEC variations and performance of the IRI model over Mineral Area community College (MACC) in 2013.

about 68 TECU during 2014 and drops again to about 35 TECU in 2015 in the same month. Moreover, the seasonal highest peak measured VTEC values of about 35, 52, and

45 TECU, and modelled (IRI 2016) VTEC values of about 40, 62 and 35 TECU are observed in the March equinox in 2013, 2014 and 2015, respectively. On the other hand, the

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Fig. 3. Monthly diurnal VTEC variation and performance of the IRI model over Mineral Area community College (MACC) in 2014.

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Fig. 4. Root-mean-square deviations between the monthly GPS and IRI VTEC variations and performance of the IRI model over Mineral Area community College (MACC) in 2014.

lowest monthly and seasonal hourly modelled VTEC values are generally observed in June solstice in using IRI 2007 and IRI 2012 versions (see Figs. 1–10). In general,

the variation of the sunspot number and radio flux 10.7 cm could result in the variation of both the measured and modelled VTEC (see from ftp://ftp.swpc.noaa.gov/

Y.A. Tariku / Advances in Space Research 63 (2019) 4025–4038

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Fig. 5. Monthly diurnal VTEC variation and performance of the IRI model over Mineral Area community College (MACC) in 2015.

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Fig. 6. Root-mean-square deviations between the monthly GPS and IRI VTEC variations and performance of the IRI model over Mineral Area community College (MACC) in 2015.

pub/indices/old_indices/2013_DSD.txt, to ftp://ftp.swpc. noaa.gov/pub/indices/old_indices/2015_DSD.txt). As shown in Figs. 1–10, when the sunspot number and radio

flux 10.7 cm values increase, the VTEC values general tend to increase and vice versa. As can be seen from the link, the sunspot number and radio flux 10.7 cm values start increas-

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ing in 2013 and attain generally the highest values in 2014 and then decrease again in shifting to 2015. As shown in Figs. 1–10, similar pattern is observed in the monthly and seasonal variability of both the measured and modelled VTEC during 2013–2015. It has also been shown that the modelled VTEC values for both the monthly and seasonal VTEC variations are generally smaller than those of the measured values mostly in the time interval about 00:00–10:00 UT (18:00–04:00 LT). On the other hand, the modelled values are generally larger than the measured ones mostly after about 15:00 UT (09:00 LT) with the highest being observed in using the IRI 2016 version, especially in the December solstice months (see Figs. 1–10). Moreover, large overestimations and underestimations of the VTEC values by the model are generally observed in the December and June solstices, respectively. For example, as shown in Figs. 1–10, the highest monthly and seasonal root mean-square deviations (due to overestimation) greater than one are observed in January (2014) and in the December solstice (2013) in using the IRI 2016 version at about 20:00 UT (14:00 LT). On the other hand, the highest monthly and seasonal rootmean-square deviations (due to underestimation) of about 0.75 and 0.5 are observed in August and the March equinox, respectively in 2013 in using the IRI 2012 version. Other underestimations with similar degree of root-meansquare deviations are also observed in 2014. Since the IRI model did not include a plasmaspheric component, the underestimation of the GNSS VTEC values (especially in the nighttime hours) by the IRI VTEC may be due to the enhancement of the plasmaspheric electron content above 2000 km in the nighttime hours (Coisson et al., 2008; Aggarwal, 2011; Venkatesh et al., 2011). Of course, the plasmaspheric content does not exhibit a strong diurnal variation because it is not exposed to the same day-night solar influence as the ionospheric content. So, since it is almost constant, its impact is observed on the total TEC when the ionospheric TEC is small and the smallest values are observed during nighttime. Consequently, the percentage contribution of the plasmaspheric content to the full content is large at night. Overall, except in the daytime hours (after about 15:00 UT, 09:00 LT) in the December solstice months, small root-mean-square deviations of less than 0.5 are observed during 2013–2015, showing that all versions of the model are generally good in estimating the hourly VTEC variation with the IRI 2016 VTEC values being better to respond to the variation of the GPS VTEC values, especially in the equinoctial and June solstice months. This could be due to the two new model options for the F2peak height hmF2 and a better representation of topside ion densities at very low and high solar activities embedded in the IRI 2016 version. The two options enable the IRI 2016 model in estimating hmf2 directly and no longer through its relationship to the propagation factor M (3000)F2. As a result, the IRI 2016 model can estimate evening peaks that was not possible in the previous ver-

sions, including the IRI 2007 and IRI 2012 (Bilitza et al., 2017). As shown in Figs. 1–10, the better estimations of TEC by the IRI 2016 version are observed in the evening hours (00:00–15:00 UT, 18:00–09:00 LT), showing that the two new options enable the model to better estimate the evening electron density, and results in good estimation of the TEC in similar fashion as compared to the IRI 2007 and IRI 2012 versions. But, when the solar activity increases during the daytime hours, the IRI 2016 version generally shows large overestimation of TEC (especially in the December solstice after 15:00 UT or 09:00 LT)), because the new model options enable the model for better estimation of TEC only in the nighttime hours. Moreover, small root-mean-square deviations close to zero (especially in using IRI 2016) are observed in the September equinox in 2013–2015, proving that all the three versions of the model relatively show good performance in the September equinox, with the 2016 version showing the best. On the contrary, large root-mean-square deviations are observed in the December solstice (especially in using IRI 2016 after about 15:00 UT or 09:00 LT), revealing that the IRI 2016 version shows poor performance as compared to the IRI 2007 and IRI 2012 versions in the December solstice except in the time interval of about 03:00–13:00 UT (21:00–07:00 LT). Moreover, the root-mean-square deviations observed in using the IRI 2007 and IRI 2012 versions are very close to each other, revealing that the two versions show similar performance. 3.2. Monthly and seasonal arithmetic mean variations of GPS VTEC and performance of the IRI model The results of the monthly and seasonal arithmetic mean hourly variations are displayed in Figs. 11 and 12. As shown in the Figures, in the years 2012–2015, both the measured and modelled VTEC values generally increase when we shift from 2013 to 2014 and decrease again in shifting to 2015 with exception of some months. As described above in the diurnal variation, this pattern is also synchronized with the patter of variation of the solar activity. However, the measured VTEC values in September, and the modelled values in October and November steadily show decrement in shifting from 2013 to 2015 (see Fig. 11). Similarly, in the June solstice and September equinox, the measured values tend to steadily decrease in shifting from 2013 to 2015 (see Fig. 12). Moreover, as usual, the highest measured and modelled monthly VTEC values are generally observed in the equinox months; while the lowest measured and modelled values are observed in the June solstice months (see Fig. 11). Nevertheless, for the seasonal variation, the lowest measured values are observed in the December solstice in 2013 and 2015, and in the September equinox in 2014. In the same way, the highest modelled seasonal VTEC values are observed in the December solstice in 2013 and the lowest values are observed in the September equinox in 2014 and 2015 (see Fig. 12). It is shown that the highest seasonal

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measured VTEC value of about 25 TECU being observed in the September equinox reduces to the lowest value of about 16 TECU in the December solstice in 2013. However, the highest seasonal measured VTEC values of about 30 and 26 TECU being observed in 2014 and 2015, respec-

tively in the March equinox reduce to the lowest values of about 20 and 15 TECU, respectively in the September equinox. In the same way, the modelled seasonal VTEC values of about 18 TECU being observed in the June solstice increases to the highest value of about 26 TECU in the

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December solstice when using the IRI 2016 version in 2013. The highest and lowest seasonal IRI 2016 VTEC values of about 33 and 20 TECU are also observed in the March and September equinox, respectively in 2014. However, in 2015, the highest and lowest seasonal modelled VTEC values of about 17 and 13 TECU are observed in the March and September equinox, respectively in using the IRI 2012 version (see Fig. 12). In general, in 2013–2015, the highest (about 35 TECU) and lowest (about 12 TECU) monthly measured values are observed in March (2014) and December (2015), respectively. Similarly, the maximum (about 30 TECU) and minimum (about 13 TECU) seasonal measured VTEC values are observed in the March equinox (2014) and December solstice (2015), respectively. Moreover, the highest and lowest modelled monthly values of about 35 and 13 TECU are observed in March in 2014 and September in 2013, respectively in using the IRI 2016 version. The highest and lowest seasonal modelled VTEC values of about 33 and 13 TECU are observed in the March equinox in 2014 and September equinox in 2015 (see Figs. 11 and 12). Moreover, in the years 2012–2015, except in the December solstice and some equinoctial months large underestimations are observed with the highest being seen in using the IRI 2007 and IRI 2012 versions. It has been shown that the largest overestimations (mostly by IRI 2016) and underestimations (mostly by IRI 2007 and IRI 2012) of the VTEC values are observed in 2013 and 2015, respectively. As shown in the Figures (see the bottom panels of Figs. 11 and 12), the highest monthly and seasonal overestimations (by about 37% and 35%, respectively) are observed by the IRI 2016 version in the December solstice months in 2013. On the other hand, when the IRI 2012 version is used, the largest monthly and seasonal deviations (underestimation of the VTEC) of about 95% and 52% are observed in August and the June solstice, respectively in 2013 as shown in the bottom panels of Figs. 11 and 12. The overall results show that the underestimation or the overestimation of the VTEC variation by the IRI 2007 version is close to that of the IRI 2012 version. This shows that the performance of the IRI 2007 version in the estimation of the VTEC variation is very close to that of the IRI 2012 version. However, the IRI 2016 version somehow tends to overestimate the VTEC values as compared to the IRI 2007 and IRI 2012 versions, especially in the December solstice months. On the other hand, the IRI 2016 version is generally better as compared to the IRI 2007 and IRI 2012 versions in the equinoctial and June solstice months with exception of the largest monthly and seasonal underestimations of about 63% and 45%, respectively being observed in March and the March equinox in 2015 (see Figs. 11 and 12). On the contrary, in the December solstice months, the IRI 2007 and IRI 2012 versions are better as compared to the IRI 2016. Overall, as shown in Figs. 1–12, the pattern of those seasonal differences is mixed. Summer shows the most consistent behavior with all four summers showing

predominantly negative deviations. Winter shows fairly consistent behavior, with two winters (2013 and 2015) showing predominantly positive deviations, but winter of 2014 shows mixed behavior. Spring is less consistent with just two springs (2013 and 2015) showing predominantly negative deviations, whilst the others show mixed behavior. Finally autumn shows no consistent behavior, positive deviations in 2015, a trend from negative to positive in 2013, and mixed behavior in 2014. However, in the present work, the overall results show that the model (in all versions) is generally good in estimating VTEC variation. The past studies (e.g. Chakraborty et al., 2014; Kumar et al., 2015; Tariku, 2016) show that the agreement between the model and measured values are generally good in the mid latitude regions as smallest discrepancies in TEC variations are observed. This may be resulting from the input data available in the region (Bilitza et al., 1999). Moreover, as shown by Chartier et al. (2012) and Wang et al. (2016), the IRI 2007 and IRI 2012 versions are capable of representing the TEC at middle latitude regions, which is similar to the result obtained in this work. 4. Conclusions This paper discusses the variation of the TEC and the IRI model performance improvement over the mid latitude regions during the solar maximum (2013–2015) years. The results show that both the measured and modelled VTEC values follow normal diurnal patterns, showing the lowest at about 10:00 UT (04:00 LT) and the highest at about 20:00 UT (14:00 LT). In addition, the monthly and seasonal modelled VTEC values (especially the IRI 2016 VTEC) are generally larger than the corresponding measured values in the December solstice months. On the other hand, the modelled VTEC values (especially the IRI 2007 and IRI 2012 VTEC) are generally smaller than the measured VTEC values in the equinoctial and June solstice months. Hence, the IRI 2016 version is generally better in estimating the VTEC variations in the equinoctial and June solstice months with some exceptions when compared to the IRI 2007 and IRI 2012 versions. However, the IRI 2007 and IRI 2012 versions are better in the December solstice months. Overall, the IRI 2016 version is generally better than the IRI 2007 and IRI 2012 versions, especially in the nighttime hours when the solar activity decreases. This may be due to the two new model options for the F2-peak height hmF2 and a better representation of topside ion densities that enable the model to better estimate the evening electron density, and results in good estimation of the TEC in similar fashion as compared to the IRI 2007 and IRI 2012 versions. On the other hand, the new hmF2 model option for F2 peak height embedded in the IRI 2016 version is believed to be valuable source for deriving the neutral wind at mid latitudes (Miller et al., 1986; Richards, 1991; Dyson et al., 1997) and IRI hmF2 has been used to produce the global scale meridional wind climatology. A meridional component of the neutral wind is also

Y.A. Tariku / Advances in Space Research 63 (2019) 4025–4038

supposed to drag charged particles from the summer to the winter hemisphere. Thus, the meridional winds blowing from the equator to polar regions may result in a high ionization crest value in the December solstice in the mid latitude regions (Wu et al., 2004; Bhuyan and Borah, 2007). This could in turn result in overestimation of TEC by the IRI model during the December solstice as compared to the IRI 2007 and IRI 2012 versions. Hence, in general, the most recent version of the IRI model (IRI 2016) which was expected for better TEC estimation does not adequately capture the TEC variation; rather large overestimations are observed in the daytime hours (especially in the December solstice months after about 15:00 UT, 09:00 LT). According to Bilitza et al. (2006), the main challenge that accounts for the poor performance in TEC estimation may also link to the poor representation of the topside ionosphere in all versions of the IRI model. Acknowledgements The author is very grateful to UNAVCO, NASA, World Data Center (Kyoto University) and NOAA to have provided GPS, IRI, Dst index and daily sunspot number data. References Aggarwal, M., 2011. TEC variability near northern EIA crest and comparison with IRI model. Adv. Space Res. 48 (7), 1221–1231. https://doi.org/10.1016/j.asr.2011.05.037. Balan, N., Bailey, G.J., Jenkins, B., Rao, P.B., Moffett, R.J., 1994. Variations of ionospheric ionization and related solar fluxes during an intense solar cycle. J. Geophys. Res.: Space Phys. 99 (2), 2243–2253. Bilitza, D., Hernandez-Pajares, M., Juan, J.M., Sanz, J., 1999. Comparison between IRI and GPS-IGS derived electron content during 1991– 97. Phys. Chem. Earth (C) 24 (4), 311–319. Bilitza, D., 2001. International reference ionosphere 2000. Radio Sci. 36 (2), 261–275. Bilitza, D., 2003. International Reference Ionosphere 2000: examples of improvements and new features. Adv. Space Res. 31, 151–167. Bilitza, D., Reinisch, B., 2008a. International reference ionosphere and global navigation satellite system. Adv. Space Res. 55 (8), 1913. https://doi.org/10.1016/j.asr.2015.02.021. Bilitza, D., Reinisch, B., 2008b. International reference ionosphere 2007: improvements and new parameters. Adv. Space Res. 42 (4), 599–609. https://doi.org/10.1016/j.asr.2007.07.048. Bilitza, D., Altadill, D., Zhang, Y., Mertens, C., Truhlik, V., Richards, P., McKinnell, L., Bodo Reinisch, B., 2014. The International reference ionosphere 2012 – a model of international collaboration. J. Space Weather Space Clim. 4, A07. https://doi.org/10.1051/swsc/2014004. Bilitza, D., Altadill, D., Truhlik, V., Shubin, V., Galkin, I., Reinisch, B., Huang, X., 2017. International Reference Ionosphere 2016: from ionospheric climate to real-time weather predictions. Space Weather. https://doi.org/10.1002/2016SW001593. Bhuyan, P.K., Borah, Rashmi Rekha, 2007. TEC derived from GPS net work in India and comparison with IRI. Adv. Space Res.: Off. J. Committee Space Res. (COSPAR) 39, 830–840. Bradford, W.P., Spilker, J.J.J., 1996. Global Positioning System: Theory and applications. American Institute of Aeronautics and Astronautics, vol. I and II Washington DC, USA. Chakraborty, M., Kumar, S., Kumar, B., Guha, A., 2014. Latitudinal characteristics of GPS derived ionospheric TEC: a comparative study with IRI 2012 model. Ann. Geophys. 57 (5), A0539. https://doi.org/ 10.4401/ag-6438.

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Chartier, A.T., Mitchell, C.N., Jackson, D.R., 2012. A 12 year comparison of MIDAS and IRI 2007 ionospheric Total Electron Content. Adv. Space Res. 49 (9), 1348–1355. Ciraolo, L., Azpilicueta, F., Brunini, C., Meza, A., Radicella, S.M., 2007. Calibration errors on experimental slant total electron content (TEC) determined with GPS. J. Geodesy 81, 111–120. Coisson, P., Radicella, S.M., Ciraolo, L., Leitinger, R., Nava, B., 2008. Global validation of IRI TEC for high and medium solar activity conditions. Adv. Space Res. 42, 770–775. Dyson, P.L., Davies, T.P., Parkinson, M.L., Reeves, A.J., Richards, P.G., Fairchild, C.E., 1997. Thermospheric neutral winds at southern midlatitudes: a comparison of optical and ionosonde hmF2 methods. J. Geophys. Res. 102 (A12), 27189–27196. https://doi.org/10.1029/ 97JA02138. Ezquer, R.G., Adler, N.O., Heredia, T., 1994. Predicted and measured total electron content at both peaks of the equatorial anomaly. Radio Sci. 29 (4), 831–838. Ezquer, R.G., Jakowski, N., Jadur, C.A., 1997. Predicted and measured total electron content over Havana. J. Atm. Solar Terres. Phys. 59 (5), 591–596. Ezquer, R.G., Mosert, M., Scida´, L., Lo´pez, J.L., 2008. Peak characteristics of F2 region over Tucuma´n :predictions and measurements. J. Atmos. Sol-Terr. Phys. 70 (11–12), 1525–1532. Ezquer, R.G., Lo´pez, J.L., Scida´, L.A., Cabrera, M.A., Zolesi, B., Bianchi, C., Pezzopane, M., Zuccheretti, E., Mosert, M., 2014. Behaviour of ionospheric magnitudes of F2 region over Tucuma´n during a deep solar minimum and comparison with the IRI 2012 model predictions. J. Atmos. Solar-Terrestrial Phys. 107, 89–98. Gao, Y., Liu, L., 2002. Precise ionospheric modelling using regional GPS network data. J. Global Positioning Syst. 1 (1), 18–24. Gonzalez, W.D., Joselyn, J.A., Kamide, Y., Kroehl, H.W., Rostoker, G., Tsurutani, B.T., Vasyliunas, V.M., 1994. What is a geomagnetic storm? J. Geophys. Res. 99 (A4), 5771–5792. https://doi.org/10.1029/ 93JA02867. Gordiyenko and Yakovets, 2017. Comparison of midlatitude ionospheric F region peak parameters and topside Ne profiles from IRI2012 model prediction with ground-based ionosonde and Alouette II observations. Adv. Space Res. 60 (2), 461–474. Hartman, G.K., Leitinger, R., 1984. Range error due to ionospheric and tropical effects for signal frequencies above 100MHz. Bull Geod. 58, 109–136. Ioannides, R.T., Strangeways, H.J., 2000. Ionosphere-induced errors in GPS range finding using MQP modelling, ray-tracing and nelder-mead optimization. In: Millennium Conference on Antennas and Propagation, AP2000, vol. II, Davos, pp. 404–408, Switzerland. Kailang, D., Jianming, M., 1994. Comparison of total electron content calculated using the IRI with observations in China. J. Atmos. Terres. Phys. 56 (3), 417–422. Klobuchar, J.A., Parkinson, B.W., Spilker, J.J., 1996. Ionospheric effects on GPS, in Global Positioning System: theory and applications, American Institute of Aeronautics and Astronautics, Washington D.C. Spring, MD, 19–21 March, pp. 193–203. Komjathy, A., Langley, R.B., 1996. The effect of shell height on high precision ionospheric modelling using GPS. In: Proceedings of the 1996 IGS Workshop, Silver Spring, MD, 19-21 March, pp. 193–203. Kumar, S., Tan, E., Murti, D., 2015. Impacts of solar activity on performance of the IRI-2012 model predictions from low to mid latitudes. Earth Planets Space 67, 42. Matsushita, S., Wallace, C., 1967. Physics of Geomagnetic Phenomena, vol. 1. Academic Press, New York. Mannucci, A.J., Wilson, B.D., Yuan, D.N., Ho, C.H., Lindqwister, U.J., Runge, T.F., 1998. A global mapping technique for GPS-derived ionospheric total electron content. McNamara, L.F., 1985. The uses of total electron content measurments to validate emperical models of the ionosphere. Adv. Space Res. 5 (7), 81–90. Miller, K.L., Torr, D.G., Richards, P.G., 1986. Meridional winds in the thermosphere derived from measurement of F2 layer height. J.

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Y.A. Tariku / Advances in Space Research 63 (2019) 4025–4038

Geophys. Res. 91 (A4), 4531–4535. https://doi.org/10.1029/ JA091iA04p04531. Nahavandchi, H., Soltanpour, A., 2008. Local ionospheric modeling of GPS code and carrier phase observation. Survey Rev. 40 (309), 271– 284. Norsuzila, Y., Abdullah, M., Ismail, M., 2008. Determination of GPS total electron content using Single Layer Model (SLM) ionospheric mapping function. Int. J. Comp. Sci. Net., Sec. 8 (9), 154–169. Norsuzila, Y., Abdullah, M., Ismail, M., Zaharim, A., 2009. Model validation for Total Electron Content (TEC) at an equatorial region. Eur. J. Sci. Res. 28 (4), 642–648. Richards, P.G., 1991. An improved algorithm for determining neutral winds from the height of the F2 peak electron density. J. Geophys. Res. 96 (17), 839. https://doi.org/10.1029/91JA01467. Seemala, G., 2017. GPS-TEC analysis application, Indian Institute of Geomagnetism (IIG), http://seemala.blogspot.com/2017/09/gps-tecprogram-ver-295.html.

Tariku, Y.A., 2016. The study of variability of TEC over mid-latitude American regions during the ascending phase of solar cycle 24 (2009– 2011). Adv. Space Res. 58, 598–608. Venkatesh, K., Rama Rao, P.V.S., Saranya, P.L., Prasad, D.S.V.V.D., Niranjan, K., 2011. Vertical electron density and topside effective scale height (HT) variations over the Indian equatorial and low latitude stations. Ann. Geophys. 29, 1861–1872. https://doi.org/10.5194/angeo-29-1861-2011. Wang, C., Shi, C., Zhang, H., Fan, L., 2016. Improvement of global ionospheric VTEC maps using the IRI 2012 ionospheric empirical model. J. Atmos. Solar-Terrestrial Phys. 146, 186–193. Wu, C.C., Fry, C.D., Liu, J.Y., Liou, K., Tseng, C.L., 2004. Annual TEC variation in the equatorial anomaly region during the solar minimum: September 1996-August 1997. J. Atmos. Terr. Phys. 66, 199–207.